High Surface Area Mesoporous Silicon Nanoparticles Prepared via Two-Step Magnesiothermic Reduction for Stoichiometric CO<sub>2</sub> to CH<sub>3</sub>OH Conversion
Bibliographic record
Abstract
Magnesiothermic reduction of silicon oxide can result in the formation of nanostructured, mesoporous elemental silicon (mp-Si), which has been explored in a variety of energy applications such as Li-ion battery anodes, photocatalytic water splitting, CO2 reduction, drug delivery vehicles, and sensors as well as for gas storage. The physical properties of the resultant mp-Si generated via magnesiothermic reduction, and thus the potential utility, are highly dependent on the specific reduction conditions utilized. Herein, we report a modified magnesiothermic reduction method which allows for the synthesis of high surface area mp-Si nanoparticles. The reaction was initiated at 650 °C and then cooled to a lower temperature to minimize heat-induced morphological damage. The nanoparticles were characterized by using powder X-ray diffraction, scanning and transmission electron microscopies, and N2 adsorption isotherm measurements. Particles prepared by using two-step annealing with the initial processing condition of 650 °C for 30 min followed by 300 °C for 4 h resulted in crystalline and completely reduced mp-Si with a high specific surface area of 542 ± 18 m2/g. mp-Si nanoparticles generated by using these specific parameters were further used for stoichiometric CO2 conversion to CH3OH, and the reaction yields were 2.5 times higher than prior reports, demonstrating usefulness in effecting an important chemical transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".